This stacked cell design works well for spectro-temporal representations since their dimensions are close to those used typically in image classification tasks to which DARTS was first applied [1]}, [2]}. For speech classification tasks and for solutions that operate upon raw inputs, however, the feature dimension remains large at the stacked cell output and the use of global pooling will result in the substantial loss of information. While a larger number of reduction cells can be added manually to help reduce the feature dimension, this would defeat the purpose of searching the architecture automatically. The introduction of each additional reduction cell also doubles the number of channels, which in turn increases prohibitively both computational complexity as well as demands upon GPU memory.
